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On the evaluation of structural equation models

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TLDR
In this article, structural equation models with latent variables are defined, critiqued, and illustrated, and an overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence.
Abstract
Criteria for evaluating structural equation models with latent variables are defined, critiqued, and illustrated. An overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence. Model assessment is considered to be a complex process mixing statistical criteria with philosophical, historical, and theoretical elements. Inevitably the process entails some attempt at a reconcilation between so-called objective and subjective norms.

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Motives and performance outcomes of sustainable supply chain management practices: a multi-theoretical perspective

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Organizational members’ use of social networking sites and job performance

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Coping with a natural disaster: Losses, emotions, and impulsive and compulsive buying

TL;DR: In this paper, a structural model based on life event theory is proposed and empirically tested using data collected from 427 US Gulf Coast residents who were impacted by Hurricane Katrina, and it is shown that perceived lack of control and loss of possessions contribute directly to stress, and event-induced stress impacts depression.
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Organizational socialization of service customers

TL;DR: In this article, the authors considered the organizational socialization of the customers of service organizations and found that customer satisfaction was positively related to customer technical and functional quality, while organizational climate and motivational direction were positively related with customer technical quality.
References
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Journal ArticleDOI

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
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A new look at the statistical model identification

TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
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Estimating the Dimension of a Model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI

Significance tests and goodness of fit in the analysis of covariance structures

TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.
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